2,940 research outputs found

    Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications

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    In the era when the market segment of Internet of Things (IoT) tops the chart in various business reports, it is apparently envisioned that the field of medicine expects to gain a large benefit from the explosion of wearables and internet-connected sensors that surround us to acquire and communicate unprecedented data on symptoms, medication, food intake, and daily-life activities impacting one's health and wellness. However, IoT-driven healthcare would have to overcome many barriers, such as: 1) There is an increasing demand for data storage on cloud servers where the analysis of the medical big data becomes increasingly complex, 2) The data, when communicated, are vulnerable to security and privacy issues, 3) The communication of the continuously collected data is not only costly but also energy hungry, 4) Operating and maintaining the sensors directly from the cloud servers are non-trial tasks. This book chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog Computing is a service-oriented intermediate layer in IoT, providing the interfaces between the sensors and cloud servers for facilitating connectivity, data transfer, and queryable local database. The centerpiece of Fog computing is a low-power, intelligent, wireless, embedded computing node that carries out signal conditioning and data analytics on raw data collected from wearables or other medical sensors and offers efficient means to serve telehealth interventions. We implemented and tested an fog computing system using the Intel Edison and Raspberry Pi that allows acquisition, computing, storage and communication of the various medical data such as pathological speech data of individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate estimation, and Electrocardiogram (ECG)-based Q, R, S detection.Comment: 29 pages, 30 figures, 5 tables. Keywords: Big Data, Body Area Network, Body Sensor Network, Edge Computing, Fog Computing, Medical Cyberphysical Systems, Medical Internet-of-Things, Telecare, Tele-treatment, Wearable Devices, Chapter in Handbook of Large-Scale Distributed Computing in Smart Healthcare (2017), Springe

    Population ageing and ICT: An exploratory review of technology innovation trough digital applications

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    The multidimensional process of physical, psychological, and social change produced by population ageing affects not only the quality of life of elderly people but also of our societies. Some dimensions of population ageing grow and expand over time (e.g. knowledge of the world events, or experience in particular situations), while others decline (e.g. reaction time, physical and psychological strength, or other functional abilities like reduced speed and tiredness). Information and Communication Technologies (ICTs) can help elderly to overcome possible limitations due to ageing. As a particular case, biometrics can allow the development of new algorithms for early detection of cognitive impairments, by processing continuous speech, handwriting or other challenged abilities. Among all possibilities, digital applications (Apps) for mobile phones or tablets can allow the dissemination of such tools. In this article, after presenting and discussing the process of population ageing and its social implications, we explore how ICTs through different Apps can lead to new solutions for facing this major demographic challenge

    Context-aware system for cardiac condition monitoring and management: a survey

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    Health monitoring assists physicians in the decision-making process, which in turn, improves quality of life. As technology advances, the usage and applications of context-aware systems continue to spread across different areas in patient monitoring and disease management. It provides a platform for healthcare professionals to assess the health status of patients in their care using multiple relevant parameters. In this survey, we consider context-aware systems proposed by researchers for health monitoring and management. More specifically, we investigate different technologies and techniques used for cardiac condition monitoring and management. This paper also propose "mCardiac", an enhanced context-aware decision support system for cardiac condition monitoring and management during rehabilitation

    Advances in automated tongue diagnosis techniques

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    This paper reviews the recent advances in a significant constituent of traditional oriental medicinal technology, called tongue diagnosis. Tongue diagnosis can be an effective, noninvasive method to perform an auxiliary diagnosis any time anywhere, which can support the global need in the primary healthcare system. This work explores the literature to evaluate the works done on the various aspects of computerized tongue diagnosis, namely preprocessing, tongue detection, segmentation, feature extraction, tongue analysis, especially in traditional Chinese medicine (TCM). In spite of huge volume of work done on automatic tongue diagnosis (ATD), there is a lack of adequate survey, especially to combine it with the current diagnosis trends. This paper studies the merits, capabilities, and associated research gaps in current works on ATD systems. After exploring the algorithms used in tongue diagnosis, the current trend and global requirements in health domain motivates us to propose a conceptual framework for the automated tongue diagnostic system on mobile enabled platform. This framework will be able to connect tongue diagnosis with the future point-of-care health system

    A Research on the Classification and Applicability of the Mobile Health Applications

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    Mobile health applications are applied for different purposes. Healthcare professionals and other users can use this type of mobile applications for specific tasks, such as diagnosis, information, prevention, treatment, and communication. This paper presents an analysis of mobile health applications used by healthcare professionals and their patients. A secondary objective of this article is to evaluate the scientific validation of these mobile health applications and to verify if the results provided by these applications have an underlying sound scientific foundation. This study also analyzed literature references and the use of mobile health applications available in online application stores. In general, a large part of these mobile health applications provides information about scientific validation. However, some mobile health applications are not validated. Therefore, the main contribution of this paper is to provide a comprehensive analysis of the usability and user-perceived quality of mobile health applications and the challenges related to scientific validation of these mobile applications.This work was funded by FCT/MCTES through national funds and when applicable co-funded EU funds under the project UIDB/EEA/50008/2020 (Este trabalho é financiado pela FCT/MCTES através de fundos nacionais e quando aplicável cofinanciado por fundos comunitários no âmbito do projeto UIDB/EEA/50008/2020)

    Android security: analysis and applications

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    The Android mobile system is home to millions of apps that offer a wide range of functionalities. Users rely on Android apps in various facets of daily life, including critical, e.g., medical, settings. Generally, users trust that apps perform their stated purpose safely and accurately. However, despite the platform’s efforts to maintain a safe environment, apps routinely manage to evade scrutiny. This dissertation analyzes Android app behavior and has revealed several weakness: lapses in device authentication schemes, deceptive practices such as apps covering their traces, as well as behavioral and descriptive inaccuracies in medical apps. Examining a large corpus of applications has revealed that suspicious behavior is often the result of lax oversight, and can occur without an explicit intent to harm users. Nevertheless, flawed app behavior is present, and is especially problematic in apps that perform critical tasks. Additionally, manufacturer’s and app developer’s claims often do not mirror actual functionalities, e.g., as we reveal in our study of LG’s Knock Code authentication scheme, and as evidenced by the removal of Google Play medical apps due to overstated functionality claims. This dissertation makes the following contributions: (1) quantifying the security of LG’s Knock Code authentication method, (2) defining deceptive practices of self-hiding app behavior found in popular apps, (3) verifying abuses of device administrator features, (4) characterizing the medical app landscape found on Google Play, (5) detailing the claimed behaviors and conditions of medical apps using ICD codes and app descriptions, (6) verifying errors in medical score calculator app implementations, and (7) discerning how medical apps should be regulated within the jurisdiction of regulatory frameworks based on their behavior and data acquired from users

    Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data

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    Audio signals generated by the human body (e.g., sighs, breathing, heart, digestion, vibration sounds) have routinely been used by clinicians as indicators to diagnose disease or assess disease pro- gression. Until recently, such signals were usually collected through manual auscultation at scheduled visits. Research has now started to use digital technology to gather bodily sounds (e.g., from dig- ital stethoscopes) for cardiovascular or respiratory examination, which could then be used for automatic analysis. Some initial work shows promise in detecting diagnostic signals of COVID-19 from voice and coughs. In this paper we describe our data analysis over a large-scale crowdsourced dataset of respiratory sounds collected to aid diagnosis of COVID-19. We use coughs and breathing to under- stand how discernible COVID-19 sounds are from those in asthma or healthy controls. Our results show that even a simple binary machine learning classifier is able to classify correctly healthy and COVID-19 sounds. We also show how we distinguish a user who tested positive for COVID-19 and has a cough from a healthy user with a cough, and users who tested positive for COVID-19 and have a cough from users with asthma and a cough. Our models achieve an AUC of above 80% across all tasks. These results are preliminary and only scratch the surface of the potential of this type of data and audio-based machine learning. This work opens the door to further investigation of how automatically analysed respiratory patterns could be used as pre-screening signals to aid COVID-19 diagnosis.ER

    Automatic Performance Status Evaluation and Physical Activity Recognition in Cancer Patients for Medical Diagnosis Assistance

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    Sobresaliente (10)The evaluation of cancer patients’ recovery is still under a big grade of subjectivity from the physicians’ diagnoses. Different systems have been successfully implemented for general physical activity evaluation, nonetheless there is still a big leap of improvement into Performance Status (PS) evaluation with ECOG and Karnofsky’s Performance Status (KPS) scores. In this project an automatic system for patients’ biomonitoring based on Android technology with smartphones and wearables has been designed. As a result, objective data is provided for the oncologists’ diagnoses along with new algorithms for physical activity and PS assessment, having the latter applied to ECOG and KPS no precedent known. Furthermore, the basics for prospective implementation of gamification has been designed for boosting patients’ motivation in their recovery.La evaluación de la recuperación de pacientes con cáncer está caracterizada por un alto grado de subjetividad en los diagnósticos del personal médico. Se han implementado con éxito diferentes sistemas para la evaluación de la actividad fı́sica, sin embargo, aún existe un amplio margen de evolución dentro de la medida de la capacidad funcional con las escalas ECOG y de Karnofsky. En este proyecto se ha diseñado un sistema automático para la biomonitorización de pacientes basado en tecnologı́a Android con smartphones y wearables. Con esto se provee a los oncólogos de datos objetivos para sus diagnósticos junto con nuevos algoritmos para la evaluación de la actividad fı́sica y la capacidad funcional, estos últimos aplicados a ECOG y la escala de Karnofsky sin precedente alguno. Además, se han sentado las bases y el diseño de una futura implementación de gamificación para favorecer la motivación del paciente en su recuperación.Beca Iniciación a la Investigación de la Universidad de GranadaDepartamento de Arquitectura y Tecnología de Computadores, Universidad de Granad
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